1. Field of the Invention
This invention generally relates to methods and systems for design alteration for wafer inspection.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Every fab is interested in detecting defects that are relevant to yield. To achieve this, defect engineers utilize various approaches in defining knowledge-based inspection care area and binning approaches. As such, design-based inspection and binning have been widely adopted by advanced fabs in the semiconductor industry. However, despite these efforts, defect data is still convoluted with nuisance defect data, which impacts defect binning, resulting in many nuisance bins among the pattern groups.
Existing methods for removing nuisance defects involve much effort in recipe setup and post-processing. In setup, smaller care areas (CAs) may be defined based on user knowledge and building of a defect organizer such as iDO, which is commercially available from KLA-Tencor, Milpitas, Calif. In post-processing, various inspection attributes such as energy and contrast or design-based binning methods are used to filter out nuisance defects. Although some filtering may be applied to defect detection results prior to binning, significant numbers of nuisance defects are included in the defect detection results used for binning. In the case of design-based binning, binning can lead to thousands of groups since all patterns (whether critical or not) are used to generate pattern groups. Such a huge number of defect groups creates a significant challenge in identifying critical pattern types among relatively large numbers of groups. For example, the number of groups may be substantially high and contain a substantially large number of nuisance bins, making it difficult to isolate important pattern groups. While design-based binning provides significant advantages over other types of defect binning methods, such binning introduces additional challenges and time to process data upon inspection. There is also no easy way to remove the nuisance bins, and computing resources used to bin defects into the nuisance groups are essentially wasted and reduce productivity.
Accordingly, it would be advantageous to develop systems and/or methods that do not have one or more of the disadvantages described above.